SigTools: Exploratory Visualization for Genomic Signals
Bibliographic record
Abstract
Abstract Motivation With the advancement of sequencing technologies, genomic data sets are constantly being expanded by high volumes of different data types. One recently introduced data type in genomic science is genomic signals, which are usually short-read coverage measurements over the genome. An example of genomic signals is Epigenomic marks which are utilized to locate functional and nonfunctional elements in genome annotation studies. To understand and evaluate the results of such studies, one needs to understand and analyze the characteristics of the input data. Results SigTools is an R-based genomic signals visualization package developed with two objectives: 1) to facilitate genomic signals exploration in order to uncover insights for later model training, refinement, and development by including distribution and autocorrelation plots. 2) to enable genomic signals interpretation by including correlation , and aggregation plots. Moreover, Sigtools also provides text-based descriptive statistics of the given signals which can be practical when developing and evaluating learning models. We also include results from 2 case studies. The first examines several previously studied genomic signals called histone modifications. This use case demonstrates how SigTools can be beneficial for satisfying scientists’ curiosity in exploring and establishing recognized datasets. The second use case examines a dataset of novel chromatin state features which are novel genomic signals generated by a learning model. This use case demonstrates how SigTools can assist in exploring the characteristics and behavior of novel signals towards their interpretation. In addition, our corresponding web application, SigTools-Shiny, extends the accessibility scope of these modules to people who are more comfortable working with graphical user interfaces instead of command-line tools. Availability SigTools source code, installation guide, and manual is available on http://github.com/shohre73 . Contact shohre_masoumi@sfu.ca
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".